Implicit Learning in Osteocyte Network under Mechanical Loading

机械负载下骨细胞网络的内隐学习

• 批准号: 7827060
• 负责人: X. Edward GUO
• 金额: $ 49.88万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-22 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7827060
• 关键词: Address; Affect; Animals; Area; Behavior; Benign; Biomechanics; Bone Marrow; Bone Resorption; Bone Surface; Bone remodeling; Ca(2+)-Calmodulin Dependent Protein Kinase; Calcium; Calcium Channel; Calcium Oscillations; Calcium Signaling; Cell Proliferation; Cells; Characteristics; Dendrites; Environment; Etiology; GTP-Binding Proteins; Gap Junctions; Goals; Grant; Hormones; In Situ; In Vitro; Knowledge; Learning; Life; Light; Liquid substance; Mechanical Stimulation; Mechanics; Membrane; Memory; Mesenchymal Stem Cells; Modeling; Molecular; Monitor; Nervous system structure; Neurons; Osteoblasts; Osteoclasts; Osteocytes; Osteoporosis; P2Y2 receptor; Pathway interactions; Pattern; Pharmaceutical Preparations; Physiological; Plastics; Process; Property; Proteins; Research; Role; Short-Term Memory; Signal Transduction; Small Interfering RNA; Stimulus; Synapses; System; Techniques; Testing; Time; Translational Research; base; bone; bone cell; bone loss; classical conditioning; computerized data processing; experience; fluid flow; implicit memory; in vivo; inhibitor/antagonist; innovation; intercellular communication; long term memory; network models; novel; prevent; response; sensor

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (15) "Translational Science" and specific Challenge Topic "15-AR-104 Bone and the Nervous System". Bone adaptation requires osteocytes to detect mechanical signals in situ and integrate the signals in the osteocytic network into appropriate activities in the bone cell system. It is conjectured that a bone cell network mimics a simple neuronal system and can acquire short-term memory to in vivo biomechanical stimuli through the elementary forms of implicit learning, e.g., habituation and sensitization. However, none of these behaviors have been confirmed experimentally in bone cell networks, and little is known about how bone cells behave as a neuronal network. The osteocytic network, recognized as the major mechanical sensor in the bone remodeling process, is the most capable candidate to accommodate the memory function in bone. In this study, we hypothesize that "the ability for elementary forms of plastic change in response to mechanical stimulation is an inherent and fundamental property of the osteocytic network, and the short-term memory in osteocytic networks can be achieved by three elementary forms of implicit learning: habituation, sensitization, and classical conditioning". In the light of our novel in vitro osteocytic network model and experience in calcium signaling research of bone cells, the goals of this study are to: (1) investigate the habituation, sensitization, and classical conditioning behaviors of osteocytic networks to fluid shear stimuli by recording and analyzing the real-time [Ca2+]i wave propagation inside the cell network and to determine the benign and noxious mechanical stimuli in the implicit learning process of osteocytic networks; and (2) investigate the roles of CaMK pathway, gap junction intercellular communication, and P2Y2 receptor in the habituation, sensitization, and classical conditioning of osteocytic networks to mechanical stimuli by using pharmacological inhibitors or siRNA-based knockdown of one of the 3 proteins. If the significant challenges associated with the memory storage in osteocytic network can be overcome, we will have an opportunity to initiate a new paradigm in research of bone mechanotransduction and behavior studies of fundamental physiological systems. The knowledge will profoundly benefit the hunt for strategies and new drugs for the treatment of osteoporosis, which affects millions of people with more than a $10 billion financial burden. This RC1 application proposes to test a novel hypothesis that the implicit memory exists in osteocyte networks under mechanical loading. The real-time intracellular calcium waves in response to various patterns of fluid flow stimulation will be analyzed to study the three elementary forms of implicit learning, i.e., habituation, sensitization, and classical conditioning, in in vitro osteocytic networks. The mechanotransduction mechanisms have a great importance in understanding the etiology of osteoporosis and preventing bone loss in space.

描述（由申请人提供）：此申请涉及广泛的挑战领域（15）“转化科学”和特定的挑战主题“ 104-Ar-104骨骼与神经系统”。骨适应需要骨细胞来检测原位机械信号，并将骨细胞网络中的信号整合到骨细胞系统中的适当活动中。据推测，骨细胞网络模拟了一个简单的神经元系统，并且可以通过隐式学习的基本形式（例如习惯和敏化）获得短期记忆以体内生物力学刺激。但是，这些行为均未在骨细胞网络中实验证实，并且对骨细胞如何作为神经元网络的行为知之甚少。骨细胞网络被认为是骨重塑过程中主要的机械传感器，是最有能力适应骨骼记忆功能的候选者。在这项研究中，我们假设“响应机械刺激的基本形式的塑性变化的能力是骨细胞网络的固有和基本特性，并且可以通过三种基本形式的隐式学习：习惯，敏化，敏化和经典条件来实现骨细胞网络中的短期记忆。鉴于我们新颖的体外骨细胞网络模型和骨细胞钙信号研究的经验，这项研究的目标是：（1）研究：（1）研究骨细胞网络对骨细胞网络的习惯性，敏感性和经典调节行为，以通过记录和分析实时的imine the Insim in nonipe [CA2+] I I内部[CA2+] I I内部[CA2+] I I内部[CA2+] I I内部[CA2+]骨细胞网络的过程； （2）研究CAMK途径，间隙连接间交流和P2Y2受体在使用药理抑制剂或基于SIRNA的敲低3蛋白的敲低的习惯，敏化和经典调节中的P2Y2受体在机械刺激中的作用。如果可以克服与骨细胞网络中的记忆存储相关的重大挑战，我们将有机会在研究骨骼机械转导和基本生理系统的行为研究方面启动新的范式。这些知识将深深地利用寻找策略和新药物来治疗骨质疏松症的知识，这影响了数百万美元的经济负担超过100亿美元的人。该RC1应用建议测试一个新的假设，即在机械载荷下存在隐式存储器存在于骨细胞网络中。将分析针对各种流体流动刺激模式的实时细胞内钙波，以研究体外骨细胞网络中的三种基本形式的隐式学习形式，即习惯，敏感和经典调节。机械转导机制在理解骨质疏松症的病因和防止太空骨质流失方面非常重要。